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Found 8 papers, 4 papers with code
Class-Incremental Grouping Network for Continual Audio-Visual Learning
Our CIGN leverages learnable audio-visual class tokens and audio-visual grouping to continually aggregate class-aware features.
Audio-Visual Class-Incremental Learning
We demonstrate that joint audio-visual modeling can improve class-incremental learning, but current methods fail to preserve semantic similarity between audio and visual features as incremental step grows.
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
This efficiency, coupled with its state-of-the-art performance, highlights LaFiCMIL's potential as a groundbreaking approach in the field of large file classification.
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Representation learning of source code is essential for applying machine learning to software engineering tasks.
Lifting Imbalanced Regression with Self-Supervised Learning
In this paper, we work on a seamless marriage of imbalanced regression and self-supervised learning.
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0. 557 to 0. 718 and a recall between 0. 562 and 0. 854 in identifying correct patches.
STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction
As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities.
Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction
However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations.
